VIPER: Variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies

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Abstract

We develop a method, VIPER, to impute the zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single-cell level. VIPER is based on nonnegative sparse regression models and is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of our method is its ability to preserve gene expression variability across cells after imputation. We illustrate the advantages of our method through several well-designed real data-based analytical experiments.

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Chen, M., & Zhou, X. (2018). VIPER: Variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies. Genome Biology, 19(1). https://doi.org/10.1186/s13059-018-1575-1

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